December 14, 2016

We describe and evaluate a character-level tagger for language-independent Named Entity
Recognition (NER). Instead of words, a sentence is represented as a sequence of characters.
The model consists of stacked bidirectional LSTMs which inputs characters and outputs tag
probabilities for each character. These probabilities are then converted to consistent word level
named entity tags using a Viterbi decoder. We are able to achieve close to state-of-the-art NER
performance in seven languages with the same basic model using only labeled NER data and no
hand-engineered features or other external resources like syntactic taggers or Gazetteers.